Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2020-09-28 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problem of long time-consuming and high equipment energy consumption for task scheduling in mobile cloud computing environment, a task scheduling strategy based on improved bird group algorithm is proposed, a task scheduling strategy based on Improved Bird Swarm Algorithm (IBSA) is proposed. Firstly, a mobile cloud task scheduling model based on energy consumption and time is constructed. Secondly, adaptive sensing coefficients and social coefficients are proposed to prevent the algorithm from falling into a local optimum. Learning factors are optimized to optimize flight behavior and ensure that Superior ability. Finally, . the task scheduling objective function is used as the fitness function of the bird group to participate in the iterative updating of the algorithm. The simulation results show that the algorithm has good effects in mobile cloud computing task scheduling compared with ant colony algorithm, particle swarm algorithm, whale algorithm and bird swarm algorithm, which can effectively save time and reduce energy consumption.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2019-01-28 Cooperative journals: 《计算机应用研究》
Abstract： The hard threshold tracking algorithm is an important reconstruction algorithm in compressed sensing, which is essentially a least squares problem and has the disadvantages of high complexity, poor convergence and long running time. This paper introduced the Nesterov method to optimize the convex relaxation phenomenon of sparse solutions, and introduced the successive relaxation iterative method to optimize the traditional hard threshold linear equations. The theory proves that the optimization results have good convergence. Simulation experiments show that the optimized algorithm effectively reduces the complexity of the algorithm and the running time.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-08-13 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the low efficiency and low utilization rate of traditional cloud computing task scheduling algorithms, this paper proposed an improved algorithm using the improved fruit fly optimization algorithm (IFOA) and genetic algorithm (GA) for task scheduling. Firstly, the task scheduling is converted into a DAG (Directed Acyclic Graph) , and the task scheduling sequence is simplified through the kruskal algorithm. Secondly, the population of Drosophila algorithm is initialized using orthogonal arrays and quantization techniques. The boundaries of the Drosophila algorithm algorithm are processed, the exploration step size is dynamically adjusted, and the individual selection is processed using the GA algorithm. Finally, the fusion algorithm IFOA-GA is used in cloud computing task scheduling in the simulation platform. Compared with IGA, IFOA and IPSO algorithm, it has certain advantages in the comparison of four indexes of QoS, which shows that the IFOA-GA algorithm can be effective in mproving cloud computing scheduling efficiency
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-06-19 Cooperative journals: 《计算机应用研究》
Abstract： Due to the low efficiency of cloud computing resource task scheduling and uneven resource allocation, this paper combined the improved fireworks algorithm and artificial bee colony algorithm into IFWA-ABC. Firstly, this paper described the cloud computing resource task scheduling. Secondly, it used the chaotic reverse learning and Cauchy distribution to optimize the FWA initialization. The radii of the core fireworks and the non-core fireworks are optimized respectively. The optimal individuals in the FWA are obtained by improving the ABC algorithm. Finally, the IFWA-ABC algorithm is used for cloud computing task scheduling. In the simulation experiment, IFWA-ABC has obvious advantages compared with FWA and ABC in terms of virtual machine, execution time, consumption cost and Energy consumption index, which can effectively improve cloud computing resource allocation efficiency.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-24 Cooperative journals: 《计算机应用研究》
Abstract： In order to solve the problem of low efficiency of resource scheduling in cloud computing, it is proposed to schedule the improved chicken swarm optimization. First, the concept of reverse learning is used to initialize the chicken swarm and improve the global search capability. Secondly, the position of chick is introduced into the concept of weight value and learning factor in particle swarm optimization to improve the individual position of the flock; the individual position of the chicken swarm optimization is again optimized by the difference algorithm, and finally the whole is processed by boundary processing to prevent possible cross-border of individual locations in the optimization. In the simulation experiment, the optimized chicken swarm optimization and the basic chicken swarm optimization, the particle swarm optimization algorithm and the ant colony algorithm are compared in terms of completion time, cost, energy consumption and load balance, and good results have been achieved.
Hits
Hits
Downloads
Comment